Exemptions in Cook County - Tax Year 2021

Supporting Material for the Exemption Report

Code
library(tidyverse)
library(httr)
library(jsonlite)
library(glue)
library(sf)
library(DT)
library(flextable)


knitr::opts_chunk$set(warning = FALSE, message = FALSE)

options(scipen = 999)

nicknames <- readxl::read_excel("../Necessary_Files/muni_shortnames.xlsx")

muni_sums <- read_csv("../Output/ptaxsim_muni_level_2006to2023.csv") |> 
 left_join(nicknames)


muni_mc_sums <- read_csv("../Output/ptaxsim_muni_MC_2006to2023.csv") |>
  rename_all(~str_replace(., "muni_mc_", ""))


muni_cl_sums <- read_csv("../Output/ptaxsim_muni_class_summaries_2006to2023.csv") |>
  left_join(nicknames)

# Shapefiles are necessary for maps

muni_shp <- read_sf("https://gis.cookcountyil.gov/traditional/rest/services/politicalBoundary/MapServer/2/query?outFields=*&where=1%3D1&f=geojson")

Cook County’s Use of Homestead Exemptions

County-Wide Exemption Summary Statistics (Tax Year 2021)

Code
year_examples <- c(2021)

for(i in year_examples){
  tbl <- muni_cl_sums |> 
  filter(year == i) |>
  group_by(year) |> 
  summarize(AV = sum(muni_c_av), 
            EAV = sum(muni_c_eav), 
            Eq_AV = sum(muni_c_equalized_av),
            'Taxed EAV' = sum(muni_c_current_taxable_eav),
            'All Exemptions' = sum(muni_c_all_exemptions), 
            'GHE' = sum(muni_c_exe_homeowner), 
            'Senior Exemp.' = sum(muni_c_exe_senior), 
            'Freeze Exemp.' = sum(muni_c_exe_freeze), 
            'PINs in Muni' = sum(muni_c_pins_in_muni),
            'PINs with Exemptions' = sum(muni_c_has_HO_exemp))  |> 
  pivot_longer(cols = c(AV:'PINs with Exemptions'), 
               names_to = "Totals", values_to = "Values")
}

tbl |> flextable::flextable()

year

Totals

Values

2,021

AV

70,733,758,749

2,021

EAV

212,387,387,384

2,021

Eq_AV

212,392,258,554

2,021

Taxed EAV

175,164,233,392

2,021

All Exemptions

16,110,627,371

2,021

GHE

10,161,823,944

2,021

Senior Exemp.

2,627,007,663

2,021

Freeze Exemp.

3,229,599,122

2,021

PINs in Muni

1,864,594

2,021

PINs with Exemptions

1,028,964

Table 1.1

Eq_AV is the 2021 equalization factor multiplied by the assessed value of all property and can be thought of as “EAV pre-exemption.” The general homestead exemption accounts for the vast majority of exempt EAV.

Figure 3.

Code
year_examples <- c(2021)

for(i in year_examples){

median_exempt <- muni_sums |>
  filter(year==i)|>
  mutate(pct_fmv_exempt = muni_fmv_exempt / muni_fmv
) |> 
  select(pct_fmv_exempt)

midpoint = scales::percent(median(median_exempt$pct_fmv_exempt), accuracy = 0.01)

print(muni_sums |>
  filter(year==i)|>
  mutate(pct_fmv_exempt = muni_fmv_exempt / muni_fmv ) |>
    mutate(agency_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name) ) |>
 full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) |>
  ggplot(aes(fill = pct_fmv_exempt)) + 
  geom_sf(aes(geometry = geometry), color = "black") + 
  theme_void()+ 
  labs(title = paste0(i), 
       subtitle =  "Exempt FMV as a Percent of Municipality-Wide FMV",
       caption = sprintf("The County-wide municipal-median is %s", midpoint)) +
    theme_void() + 
  theme(axis.ticks = element_blank(), 
        axis.text = element_blank())+
   scale_fill_steps2(high = "darkblue", low = "black",  mid = "beige",
                       n.breaks = 7, show.limits=TRUE,
                       na.value = NA,
                    nice.breaks = FALSE,
                   midpoint = median(median_exempt$pct_fmv_exempt),
                        name = "% Exempt", label = scales::percent))
}
Code
year_examples <- c(2021)

for(i in year_examples){

median_exempt <- muni_sums |>
  filter(year==i)|>
  mutate(pct_fmv_exempt = muni_fmv_exempt / muni_fmv
) |> 
  select(pct_fmv_exempt)

midpoint = scales::percent(median(median_exempt$pct_fmv_exempt), accuracy = 0.01)

print(muni_sums |>
  filter(year==i)|>
  mutate(pct_fmv_exempt = muni_fmv_exempt / muni_fmv ) |>
    mutate(agency_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name) ) |>
 full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) |>
  ggplot(aes(fill = pct_fmv_exempt)) + 
  geom_sf(aes(geometry = geometry), color = "black") + 
  theme_void()+ 
  labs(title = paste0(i), 
       subtitle =  "Exempt FMV as a Percent of Municipality-Wide FMV"#,
       #caption = sprintf("The County-wide municipal-median is %s", midpoint)
       ) +
  theme(axis.ticks = element_blank(), 
        axis.text = element_blank())+
   scale_fill_steps(high = "darkblue", low = "pink",
                       n.breaks = 5, show.limits=TRUE,
                    limits = c(0, 0.35),
                       na.value = "gray50",
                    nice.breaks = FALSE,
                        name = "% Exempt", label = scales::percent))
}

The County-wide municipal-median percent of FMV exempt from property taxes in 2021 was 14.40% (Muni Name).

The percent of municipal EAV exempt from property taxes are greatest across Cook County’s south suburbs and lowest in the northermost suburbs of the county.

Figure 4. Exempt EAV: City vs. Suburbs

Code
muni_cl_sums |> 
  filter(year == 2021) |> 
  select(clean_name, muni_c_exe_homeowner:muni_c_exe_vet_dis) |>
  mutate(clean_name = ifelse(is.na(clean_name), "Unincorporated", clean_name),
    Geography = ifelse(clean_name == "Chicago", "City", "Suburbs"),
         Geography = ifelse(is.na(clean_name), "Unincorporated", Geography),
         Geography = factor(Geography, levels = c("Suburbs", "City", "Unincorporated"))) |>
  group_by(Geography) |>
  summarize("GHE" = sum(muni_c_exe_homeowner, na.rm=TRUE),
            "Senior Homestead" = sum(muni_c_exe_senior, na.rm=TRUE),
            "Senior Freeze" = sum(muni_c_exe_freeze, na.rm=TRUE),
            "Other Exemptions" = sum(muni_c_exe_longtime_homeowner+muni_c_exe_disabled+muni_c_exe_vet_returning+ muni_c_exe_vet_dis_lt50 + muni_c_exe_vet_dis_50_69+muni_c_exe_vet_dis_ge70)) |>
  pivot_longer(cols = c(`GHE`:`Other Exemptions`), names_to = "Type" ) |>
   mutate(Type = factor(Type, levels = c("GHE", "Senior Homestead", "Senior Freeze", "Other Exemptions" 
                                         #"Senior Freeze", "Senior Homestead", "GHE"
                                        )
                                        )) |>
  ggplot(aes(x=Type, y = value/1000000000, fill = Geography
             )) +
  
  geom_col(
           position = "dodge" 
          ) + 
  theme_minimal() +
  labs(title = "Exempt EAV in Cook County, Tax Year 2021", 
       subtitle = "by Type and Geography", 
       y = "EAV (Billions)", x = "Exemption Type") + 
  scale_y_continuous() +
  scale_fill_manual(values = c("blue3", "deepskyblue3", "gray"), )

Table 1.2: Exempt EAV in Cook County, Tax Year 2021 Exemption use varies between suburban Cook County ($9.7B EAV) and the City of Chicago ($7.0B EAV), which may point to broader socioeconomic patterns as well as residents’ eligibility and update. Note: Our current calculations undervalue the disabled veterans exemption.

Exemption use varies between suburban Cook County ($9.7B EAV) and the City of Chicago ($7.0B EAV), which may point to broader socioeconomic patterns as well as residents’ eligibility and update.

Figure 5. Value of residential exemptions by type in Cook County over time, Tax Years 2006 - 2021

Code
muni_cl_sums |> 
  select(year, muni_c_exe_homeowner:muni_c_exe_vet_dis) |>
  filter(year <= 2021) |>
  group_by(year) |>
  summarize("GHE" = sum(muni_c_exe_homeowner, na.rm=TRUE),
            "Senior Homestead" = sum(muni_c_exe_senior, na.rm=TRUE),
            "Senior Freeze" = sum(muni_c_exe_freeze, na.rm=TRUE),
            "Other Exemptions" = sum(muni_c_exe_longtime_homeowner + muni_c_exe_disabled + muni_c_exe_vet_returning+ muni_c_exe_vet_dis_lt50 + muni_c_exe_vet_dis_50_69+muni_c_exe_vet_dis_ge70, na.rm = T)) |>
  pivot_longer(cols = c(`GHE`:`Other Exemptions`), names_to = "Type" ) |>
  mutate(Type = factor(Type, levels = c(#"GHE", "Senior Homestead", "Senior Freeze", 
                                        "Other Exemptions", "Senior Freeze", "Senior Homestead", "GHE"))) |>
  ggplot(aes(x=year, y = value/1000000000, fill = Type)) +
  
  geom_bar( stat = "identity" ) + 
  theme_minimal() +
  labs(title = "Residential Exempt EAV in Cook County", 
       subtitle = "by Type and Geography, 2006-2021", 
       y = "EAV (Billions)", x ="") +
  scale_fill_manual(values = c("chartreuse2",  "darksalmon", "brown2", "blue4" )) +
                    scale_x_continuous(breaks = c(2006, 2010, 2015, 2020))

Trends in the total EAV reductions due to homestead exemptions reflect both real estate market shifts and the legislative changes in their availability, eligibility, and value.

Effect on Composite Tax Rates

NOTE: I THINK WE WANT THIS CHUNK ON THE DATA PAGE

Code
cross_county_lines <- c("030440000", "030585000", "030890000", "030320000", "031280000",
                        "030080000", "030560000", "031120000", "030280000", "030340000",
                        "030150000","030050000", "030180000","030500000", "031210000")

eq_factor <- 3.0027


incentive_majorclasses <- c("6", "7A", "7B", "8A", "8B")

commercial_classes <- c(401:435, 490, 491, 492, 496:499,
                        500:535,590, 591, 592, 597:599, 
                        700:799,
                        800:835, 891, 892, 897, 899)  

industrial_classes <- c(480:489,493, 
                        550:589, 593,
                        600:699,
                        850:890, 893
                        )

ptax_pins <- read_csv("../Output/Dont_Upload/0_Joined_PIN_data_2023.csv") |> select(-c(eq_av, propclass_1dig))


ptax_pins <- ptax_pins |> 
  mutate(class_1dig = str_sub(class, 1,1),
         class_group = case_when(
          (class_1dig == 5 & class %in% commercial_classes) ~ "5A",
          (class_1dig == 5 & class %in% industrial_classes) ~ "5B",
          class_1dig == 7 &  class < 742 ~ "7A",
          class_1dig == 7 &  class >= 742 ~ "7B",
          (class_1dig == 8 & class %in% commercial_classes ) ~ "8A",
          (class_1dig == 8 & class %in% industrial_classes ) ~ "8B",
          TRUE ~ as.character(class_1dig))) |>
  mutate(
    # taxing district revenue = taxable eav * tax rate so rearrange the formula:
    taxed_eav = final_tax_to_dist / tax_code_rate*100,
    
    total_value_eav = (final_tax_to_dist + final_tax_to_tif)/ tax_code_rate * 100 + all_exemptions + abatements,
    
    exempt_eav_inTIF = ifelse(in_tif == 1, all_exemptions, 0),
    
    exempt_eav = all_exemptions + abatements,
    
    taxed_av =  taxed_eav / eq_factor, # current value that taxing agencies can tax for their levies
    
    ## taxable AV = equalized assessed value net TIF increments, gross exemptions. 
    ## Used for calculating untaxable value further below
   # taxable_av = (final_tax_to_dist / tax_code_rate *100 + all_exemptions + abatements)/ eq_factor, 
  
    # taxable_eav_fromincents =  ifelse(class >=600 & class < 900, taxable_av * eq_factor, 0),

      ## untaxable value = exempt EAV from abatements and exemptions + TIF increment
    untaxable_value_eav = all_exemptions + abatements + 
      ## TIF increment EAV above frozen EAV, which becomes TIF revenue
      (final_tax_to_tif /  tax_code_rate*100) +
      
      ## difference between 25% and reduced level of assessment for incentive class properties. Excludes TIF increment when calculating the difference! 
      ifelse(between(class, 600, 899), 
             (taxed_av/loa*0.25 - taxed_av)*eq_factor, 0),
   
    untaxable_incent_eav = ifelse(between(class, 600, 899), 
             (taxed_av/loa*0.25 - taxed_av)*eq_factor, 0),
   
  # Class 239 property values (farms) are unequalized and reflect different farm
  # valuations (See CCAO Classification Document)
    untaxable_value_eav = ifelse(class == 239, 
                                 equalized_av-taxed_eav, untaxable_value_eav), 
    
    untaxable_value_av = untaxable_value_eav / eq_factor,
    untaxable_value_fmv = untaxable_value_av / loa,

    exempt_fmv = exempt_eav / eq_factor / loa, 
    
    fmv_inTIF = ifelse(in_tif==1, av/loa, 0),
   
    fmv_tif_increment = ifelse(final_tax_to_tif > 0, 
                               ((final_tax_to_tif / (tax_code_rate/100)) / eq_factor ) / loa, 0),
    fmv_incents_inTIF = ifelse(between(class, 600, 899) & in_tif == 1, 
                               fmv, 0),
    fmv_incents_tif_increment = ifelse(between(class, 600, 899) & final_tax_to_tif > 0 , 
                               ((final_tax_to_tif / (tax_code_rate/100)) / eq_factor ) / loa, 0),
    naive_rev_forgone =  untaxable_incent_eav * tax_code_rate/100) |>
  select(tax_code, class, pin, fmv,
         untaxable_value_fmv, fmv_inTIF, fmv_tif_increment, fmv, total_billed, final_tax_to_dist, final_tax_to_tif, tax_code_rate, eav, equalized_av, av, everything())
Code
muni_ratechange <- ptax_pins |>
  mutate(class = as.numeric(class)) |>         # Allows for joining later
 # select(-c(propclass_1dig:av.y)) |>
  filter(!clean_name  %in% c("Frankfort", "Homer Glen",  "Oak Brook", "East Dundee", "University Park",  "Bensenville", "Hinsdale", "Roselle", "Deer Park", "Deerfield")) |> 
  # filter(!agency_num %in% cross_county_lines) |>
  group_by(clean_name) |>
  summarize(
    classgroup_PC = n(),
    # projects = n_distinct(both_ids), # mostly for industrial and commercial properties
    pins_withincents = sum(ifelse(class >= 600 & class < 900, 1,0)),
    fmv_incentive = sum(ifelse(class >=600 & class <900, fmv, 0), na.rm = TRUE),
    #fmv_taxed =  sum(taxed_fmv, na.rm=TRUE),
    fmv_incents_inTIFs = sum(ifelse(class >=600 & class <900 & final_tax_to_tif > 0, fmv, 0), na.rm = TRUE),
    fmv_inTIF = sum(ifelse(final_tax_to_tif > 0, fmv, 0), na.rm=TRUE),
    fmv_tif_increment = sum(fmv_tif_increment, na.rm=TRUE),
    fmv_untaxable_value = sum(untaxable_value_fmv , na.rm=TRUE),
    fmv_exemptions = sum(all_exemptions/eq_factor/loa, na.rm=TRUE),
    fmv_abatements = sum(exe_abate/eq_factor/loa, na.rm=TRUE),
    zero_bill = sum(zero_bill, na.rm=TRUE),
    fmv_residential = sum(ifelse(class %in% c(200:399), fmv, 0), na.rm = TRUE),
    fmv_C2 = sum(ifelse(class %in% c(200:299), fmv, 0), na.rm = TRUE),
    
    fmv_industrial = sum(ifelse(class %in% industrial_classes, fmv, 0), na.rm = TRUE),
    fmv_commercial = sum(ifelse(class %in% commercial_classes, fmv, 0), na.rm = TRUE),
    
    current_rate_avg = mean(tax_code_rate),
    avg_C2_bill_noexe = mean(ifelse(between(class,200,299) & all_exemptions == 0, (final_tax_to_dist + final_tax_to_tif), NA), na.rm=TRUE),
    avg_C2_bill_withexe = mean(ifelse(between(class,200,299) & all_exemptions > 0, (final_tax_to_dist + final_tax_to_tif), NA), na.rm=TRUE),
    av_taxed = sum(taxed_av, na.rm = TRUE),
    untaxable_value_av = sum(untaxable_value_av, na.rm=TRUE),
    av = sum(av),
    eav_taxed = sum(taxed_av*eq_factor), 
    eav_untaxable = sum(untaxable_value_eav, na.rm=TRUE),
    eav_tif_increment = sum(final_tax_to_tif/tax_code_rate, na.rm=TRUE),
    eav_max = sum(fmv*loa*eq_factor, na.rm=TRUE),
    fmv = sum(fmv, na.rm=TRUE),
    pins_in_class = n(),
    all_exemptions = sum(all_exemptions),   # in EAV
    abatements = sum(exe_abate),            # in EAV
    eav_incents_inTIFs = sum(ifelse(class >=600 & class <=900 & in_tif == 1, eav, 0), na.rm = TRUE),
    final_tax_to_dist = sum(final_tax_to_dist),
    final_tax_to_tif = sum(final_tax_to_tif),
    eav = sum(eav),
    new_TEAV_noIncents = sum(ifelse(class >=600 & class <900,
                                    (taxed_av*eq_factor/loa)*0.25, taxed_av*eq_factor), na.rm=TRUE),
    
    ####### Not used currently
    # new_TEAV_noC6 = sum(ifelse( class >=600 & class <700, 
    #                             (taxed_av*eq_factor/loa)*0.25 , taxed_av*eq_factor)),
    # new_TEAV_noC7 = sum(ifelse(class >=700 & class <800,
    #                            (taxed_av*eq_factor/loa)*0.25, taxed_av*eq_factor)),
    # new_TEAV_noC8 = sum(ifelse(class >=800 & class <900, (taxed_av*eq_factor/loa)*0.25, taxed_av*eq_factor)),
    # 
    #######

     new_TEAV_vacant_noIncents = sum(ifelse(class >=600 & class <900,
                                            0, taxed_av*eq_factor))
    ) |>
  mutate(
    
    new_TEAV_noExemps = eav_taxed + all_exemptions, # does not include abatements
    new_TEAV_noAbates = eav_taxed + abatements, # include only abatements, not other exemption types
    
    # amount of EAV from taxing an additional 15% of the AV if incentive properties didn't exist
    forgone_EAV_incent = #class_group %in% incentive_majorclasses,
      #incent_prop == "Incentive", 
      new_TEAV_noIncents - eav_taxed) |>
  #cbind(table_cook) |>
  mutate(
    # Absolute maximum TEAV: No Exemptions, no abatements, no TIFS, no Incentive properties
    # Commercial and industrial assessed at 25%
    TEAV_max = eav_taxed + all_exemptions + abatements + eav_tif_increment + forgone_EAV_incent,
    
    # no exemptions or incentive classifications:
    TEAV_neither = eav_taxed + all_exemptions + forgone_EAV_incent,
    
    rate_noExe = final_tax_to_dist / new_TEAV_noExemps * 100,
    rate_noAbate = final_tax_to_dist / new_TEAV_noAbates * 100,
    rate_noInc = final_tax_to_dist / new_TEAV_noIncents * 100,
    rate_neither = final_tax_to_dist / TEAV_neither * 100, 
    rate_noTIFs = final_tax_to_dist / (eav_taxed + eav_tif_increment) * 100,
    rate_vacant = final_tax_to_dist / new_TEAV_vacant_noIncents* 100,
    rate_lowest = final_tax_to_dist / TEAV_max * 100,
    # rate_noC6 = levy / new_TEAV_noC6 * 100,
    # rate_noC7 = levy / TEAV_noC7 * 100,
    # rate_noC8 = levy / TEAV_noC8 * 100,
    rate_current = final_tax_to_dist / eav_taxed * 100,
    change_noInc = rate_current - rate_noInc,
    change_neither = rate_current - rate_neither,
    change_noTIF = rate_current - rate_noTIFs,
    change_noExe = rate_current - rate_noExe,
    change_vacant = rate_current - rate_vacant,
    change_lowest = rate_current - rate_lowest
  )  |>
     mutate(across(contains("rate_"), round, digits = 2)) |>
     mutate(across(contains("change_"), round, digits = 2))

write_csv(muni_ratechange, "../Output/muni_ratechange_2023.csv")
Code
#|eval: false
#|
muni_ratechange <- read_csv("../Output/muni_ratechange_2023.csv")

muni_ratechange |> 
  select(clean_name, starts_with("change_")) |> 
  DT::datatable(rownames = FALSE)

Figure 6. Composite property tax rates with and without homestead exemptions, tax year 2021

Figure 6 will not be recreated but is just a bar chart of the change in tax rate in the table below

Table 1. Change in composite property tax rates due to exemptions, tax year 2023.

Code
muni_ratechange |> 
  select(clean_name,  current_rate_avg, rate_noExe, change_noExe, final_tax_to_dist 
         ) |> 
  DT::datatable(rownames = FALSE, colnames = c('Municipality' = 'clean_name','Current Comp. Rate' = 'current_rate_avg', 'Hypothetical Rate' = 'rate_noExe',  'Composite Tax Rate Change'='change_noExe', 

                       'Composite Levy' = 'final_tax_to_dist'),
          caption = "Table 1: Current and Hypothetical Composite Tax Rates if GHE $0") |>
  formatCurrency('Composite Levy', digits = 0)
Table 1.3: Searchable table containing all municipalities and the hypothetical tax rate change if exempt EAV became taxable.
Code
muni_ratechange |>
  select(clean_name,  current_rate_avg, rate_noExe, change_noExe 
) |>
  arrange(desc(change_noExe)) |>
  slice(c(1:5, 69:73, 121:125)) |>
    #slice(c(1:5, 58:62, 115:119)) |>

  flextable() |> 
  border_remove() |>
  hline_top() |>
  hline(i = c(5,10)) |>

  set_header_labels(
    clean_name = "Municipality", 
    current_rate_avg = "With Exemptions",
    rate_noExe = "Without Exemptions",
    change_noExe = "Percent Point Difference"
    
    ) |>
  align(j = 2, align = "right") |>
align(j=2, align = "right", part = "header") |>
set_table_properties( layout = "autofit") |> bold(i = 8) 
Table 1.4: Includes all exemption types in calculation of rate change.

Figure 7. Map of Spatial Patterns in Composite Tax Rate Change

Change in composite tax rate if exempt EAV was added back to the the taxable tax base (i.e. if there were no exemptions).

Code
muni_ratechange  |>
  left_join(nicknames, by = "clean_name") |>
     #mutate(burden_current = ifelse(burden_current>1, 1, burden_current)) |>

    # filter(major_class_code == 2) |>
  mutate(agency_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name),
         shpfile_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name) ) |>
  full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) |>

  ggplot(aes(fill = change_noExe)) + 
  geom_sf(aes(geometry = geometry), color = "black") + 
  theme_void() + 
  theme(axis.ticks = element_blank(), axis.text = element_blank())+
   scale_fill_stepsn(colors = c( "#F7FEF5",
                                # "#e4f1e0",
                                "#d4f6cc",
                                "#47ba24",
                                "#1F6805",
                                "#133C04"),
                        show.limits=TRUE, 
                     limits = c(0, 15),
                     breaks = c(0, 2.5, 5, 7.5, 10, 15),
                     na.value = NA,
                        name = "Rate Change from \nExemptions"  
                    )

Effect on Tax Burdens

Figure 8. Dolton example of Share of levy paid by property type

CMAP used a donut chart, we will use a bar chart that represents 100% on the x a axis.

Code
taxcodes_current <- ptax_pins |> 
  group_by(tax_code) |>
  summarize(
    av = sum(av),
    eav = sum(eav),
    equalized_AV = sum(equalized_av),
    pins_in_class = n(),
    current_exemptions = sum(all_exemptions),
    HO_exemps = sum(exe_homeowner),
    tax_code_rate = first(tax_code_rate), 
    final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE), # used as LEVY amount!! 
    final_tax_to_tif = sum(final_tax_to_tif, na.rm = TRUE),
    tax_amt_exe = sum(tax_amt_exe, na.rm = TRUE), 
    tax_amt_pre_exe = sum(tax_amt_pre_exe, na.rm = TRUE), 
    tax_amt_post_exe = sum(tax_amt_post_exe, na.rm = TRUE),
    rpm_tif_to_cps = sum(rpm_tif_to_cps, na.rm = TRUE), # not used
    rpm_tif_to_rpm = sum(rpm_tif_to_rpm, na.rm=TRUE), # not used
    rpm_tif_to_dist = sum(rpm_tif_to_dist, na.rm=TRUE), # not used
    tif_share = mean(tif_share, na.rm=TRUE), # not used
  ) |>
  
  mutate(total_bill_current = final_tax_to_dist + final_tax_to_tif) |>
  mutate(cur_comp_TC_rate = tax_code_rate) |>
  mutate(current_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100),
         new_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100) + HO_exemps) |>
  mutate(new_comp_TC_rate = (final_tax_to_dist / new_taxable_eav)*100) |>
  mutate(new_comp_TC_rate = ifelse(is.nan(new_comp_TC_rate), cur_comp_TC_rate, new_comp_TC_rate)) |>
  select(tax_code, cur_comp_TC_rate, new_comp_TC_rate, current_taxable_eav, new_taxable_eav, everything())

taxcode_taxrates <- taxcodes_current |> 
  select(tax_code, cur_comp_TC_rate, new_comp_TC_rate, current_exemptions, HO_exemps)


mc_burden <- ptax_pins |>
  left_join(taxcode_taxrates, by = "tax_code") |>
  group_by(clean_name, class_1dig) |>
  
  ## calculate taxbase from each major class 
  ## and the amount of taxes currently collected from each
  summarize(group_taxbase= sum(taxed_eav),
            group_taxes_current = sum(taxed_eav * (tax_code_rate/100)),
            hyp_group_taxbase = sum(taxed_eav + all_exemptions, na.rm = T),
            hyp_group_taxes = sum( (taxed_eav + all_exemptions) * (new_comp_TC_rate/100), na.rm = T)
     )  |>
  mutate(across(c(group_taxbase:hyp_group_taxes), round, 0)) |>
  group_by(clean_name) |>
  
  mutate(
    muni_taxbase = sum(group_taxbase, na.rm=T),
    muni_levy = sum(group_taxes_current, na.rm = T)
  ) |>
  ungroup() |>
  mutate(
         pct_eav = group_taxbase / muni_taxbase,
         pct_taxburden_current = group_taxes_current / muni_levy,
         hyp_pct_taxburden = hyp_group_taxes / muni_levy) |>
  mutate(
         burden_shift = (pct_taxburden_current - hyp_pct_taxburden)*100)


burden_c2 <- mc_burden |> 
  filter(class_1dig == 2) |> 
  select(clean_name, pct_eav, burden_shift,  pct_taxburden_current, hyp_pct_taxburden) |> 
  arrange(pct_eav)

mc_burden |> 
  filter(clean_name == "Dolton") |> 
  select(class_1dig, pct_taxburden_current, hyp_pct_taxburden) |> 
  arrange(desc(pct_taxburden_current))
Table 1.5

Figure 9. Change in Share of Tax Burden

Table 2. Change in share of property tax burden

Table 2: Change in the share of property tax burden due to exemptions for single-family, multi-family, and commercial and industrial properties, tax year 2023

Code
burden_shift <- ptax_pins |>
    mutate(Group = case_when(
    class_1dig == 2 ~ "Single-family",
    class_1dig %in% c(3, 9) ~ "Multi-family",
     TRUE ~ "Commercial & Industrial"
  )) |>
    left_join(taxcode_taxrates, by = "tax_code") |>
  group_by(clean_name, Group) |>
  
  ## calculate taxbase from each major class 
  ## and the amount of taxes currently collected from each
  summarize(group_taxbase= sum(taxed_eav, na.rm = T),
            group_taxes_current = sum(taxed_eav * (tax_code_rate/100), na.rm = T),
            hyp_group_taxbase = sum(taxed_eav + all_exemptions, na.rm = T),
            hyp_group_taxes = sum( (taxed_eav + all_exemptions) * (new_comp_TC_rate/100), na.rm = T)
     )  |>
  mutate(across(c(group_taxbase:hyp_group_taxes), round, 0)) |>
  ungroup() |>
  group_by(clean_name) |>
  mutate(
    muni_taxbase = sum(group_taxbase, na.rm=T),
    muni_levy = sum(group_taxes_current, na.rm = T)
  ) |>  
  ungroup() |>
  mutate(
         pct_eav_current = group_taxbase / muni_taxbase,
         pct_taxburden_current = group_taxes_current / muni_levy,
         hyp_pct_taxburden = hyp_group_taxes / muni_levy) |> 
  mutate(
         burden_shift = (pct_taxburden_current - hyp_pct_taxburden)*100) 
Code
burden_shift <- ptax_pins |>
  filter(class_1dig != 0) |>
    mutate(Group = case_when(
    class_1dig == 2 ~ "Single-family",
    class_1dig == 3 | class_1dig == 9 ~ "Multi-family",
     TRUE ~ "Commercial & Industrial"
  )) |>
    left_join(taxcode_taxrates, by = "tax_code") |>
  group_by(clean_name) |> 
  mutate(muni_levy = sum(final_tax_to_dist, na.rm=TRUE)) |>
  ungroup() |>
  group_by(clean_name, Group, muni_levy) |>
  
  ## calculate taxbase from each major class 
  ## and the amount of taxes currently collected from each
  summarize(group_taxbase= sum(taxed_eav, na.rm = T),
            group_taxes_current = sum(taxed_eav * (cur_comp_TC_rate/100), na.rm = T),
            hyp_group_taxbase = sum(taxed_eav + all_exemptions, na.rm = T),
            hyp_group_taxes = sum( (taxed_eav + all_exemptions) * (new_comp_TC_rate/100), na.rm = T))  |>
  mutate(across(c(group_taxbase:hyp_group_taxes), round, 0)) |>
  ungroup() |>
 # group_by(clean_name) |>
 # mutate(
   # muni_taxbase = sum(group_taxbase, na.rm=T),
   # muni_levy = sum(group_taxes_current, na.rm = T)
 # ) |>  
 # ungroup() |>
  mutate(
     #    pct_eav_current = group_taxbase / muni_taxbase,
         pct_taxburden_current = group_taxes_current / muni_levy,
         hyp_pct_taxburden = hyp_group_taxes / muni_levy)  |> 
  mutate(
         burden_shift = (pct_taxburden_current - hyp_pct_taxburden))
Code
## Current tax burden 
burden_shift |>  
    select(clean_name, Group, pct_taxburden_current) |>
pivot_wider(id_cols = clean_name, names_from = Group, values_from = pct_taxburden_current) |>
  arrange((`Single-family`))
Table 1.6: Current tax burden (levy paid by group / total levy billed by municipality)

Something is wrong with burden shift calculation

Code
burden_shift |>  
    select(clean_name, Group, burden_shift) |>
pivot_wider(id_cols=clean_name, names_from = Group, values_from = burden_shift) |>
  arrange((`Single-family`))
Table 1.7: Change in tax burden, measured in percentage point change.
Code
datatable(burden_c2, rownames = FALSE,
          colnames = c('Municipality' = 'clean_name', 
                       'Burden Shift, Pct Pt Change' = 'burden_shift', 
                       "C2 EAV/Muni EAV" = 'pct_eav', 
                       'Current Tax Burden\nC2 Tax Collected / Muni Levy' = 'pct_taxburden_current', 
                       "Hypothetical Tax Burden\nHyp. C2 Tax Collected / Muni Levy" = 'hyp_pct_taxburden'),
          caption = "Table 2: Current Share of Taxable EAV and Share of Levy Paid by Class 2 Properties"
           ) |>
  formatPercentage(c(2,3,4,5), digits = 2) |>
  formatRound(c(3), digits = 2)

Figure 9 Follow up. Current Tax Burden Map

Code
burden_c2 |>
  left_join(nicknames) |>
  mutate(agency_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name) ) |>
  full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) |>

  ggplot(aes(fill = pct_taxburden_current)) + 
  geom_sf(aes(geometry = geometry), color = "black") + 
  theme_void() + 
  theme(axis.ticks = element_blank(), axis.text = element_blank())+
  # scale_fill_gradientn(
   scale_fill_stepsn(colors = c("#ffffcc","#a1dab4" ,"#41b6c4","#2c7fb8", "#253494"),
                        show.limits=TRUE, 
                     limits = c(0,1),
                     na.value = "gray70",
                     n.breaks = 6,
                        name = "Burden with \nExemptions", 
                     labels = scales::percent
                     
                    )+
  labs(title = "Current share of property tax burden", 
       subtitle = "for Class = 2 Property Types")

Table 3.

Difference in taxbills for those that do and do not claim homeowner exemption

Code
ptax_pins <- ptax_pins |> 
  left_join(taxcode_taxrates, by = "tax_code") |>
  mutate(
    bill_current = (final_tax_to_dist + final_tax_to_tif),
    bill_noGHE = new_comp_TC_rate/100*(equalized_av-all_exemptions+exe_homeowner),
    bill_noexemps = new_comp_TC_rate/100*(equalized_av),
    bill_change = bill_noGHE - bill_current)
Code
C2_munistats <- ptax_pins |> 
  filter(class > 199 & class < 300) |>
  group_by(clean_name)  |>
  arrange(av) |>
  summarize(
    median_eav = round(median(eav)), 
    median_av = round(median(av)), 
    avg_av = round(mean(av)),
    avg_eav = round(mean(eav)),
    C2_pins_in_muni = n(),
    C2_current_exemptions = sum(all_exemptions, na.rm = TRUE),
    C2_HO_exemps = sum(exe_homeowner, na.rm = TRUE),
  )

## removes properties that have more than one exemption type
C2_munistats_filtered <- ptax_pins |> 
  filter(class > 199 & class < 300) |>
    filter(exe_senior == 0 & 
           exe_freeze == 0 & 
           exe_longtime_homeowner == 0 & 
           exe_disabled == 0 & 
           exe_vet_returning == 0 & 
           exe_vet_dis_lt50 == 0 & 
           exe_vet_dis_50_69 == 0 & 
           exe_vet_dis_ge70 == 0 & 
           exe_abate == 0) |>
  group_by(clean_name)  |>
  arrange(av) |>
  summarize(
    median_eav = round(median(eav, na.rm=TRUE)), 
    median_av = round(median(av, na.rm=TRUE)), 
    avg_av = round(mean(av, na.rm=TRUE)),
    avg_eav = round(mean(eav, na.rm=TRUE)),
    C2_pins_in_muni = n(),
    C2_current_exemptions = sum(all_exemptions, na.rm = TRUE),
    C2_HO_exemps = sum(exe_homeowner, na.rm = TRUE),
  )

C2_munistats_filtered 
Code
C2_munistats
Code
## Grouped by if they have a $0 tax bill and had the GHE per muni
## Recalculating for Josh & Rachael
muni_median_summarytable <- ptax_pins |> 

  filter(class >199 & class < 300) |> 
  
  # merge in muni residential median AV
  left_join(C2_munistats_filtered) |>
  
  # +/- 500 from municpalities median residential AV
  filter(av < median_av+200 & av > median_av-200) |>
  
  # Removes properties that received other types of exemptions
  filter(exe_senior == 0 & 
           exe_freeze == 0 & 
           exe_longtime_homeowner == 0 & 
           exe_disabled == 0 & 
           exe_vet_returning == 0 & 
           exe_vet_dis_lt50 == 0 & 
           exe_vet_dis_50_69 == 0 & 
           exe_vet_dis_ge70 == 0 & 
           exe_abate == 0) |>
  
  arrange(av) |>
  
  mutate(bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_av-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) |>
  
  group_by(clean_name, # zero_bill, 
           has_HO_exemp) |> 
  
  summarize(AV = median(median_av), # median_av was calculated earlier: C2 median AV for the muni 
            `Taxable EAV` = round(median(eav)),
            bill_cur = round(median(bill_current)),
            bill_new = round(median(bill_noexemps)),
            bill_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = round(median(tax_amt_exe))) |> 
  
  # merge in clean_names variable
  left_join(nicknames) |>
  
  select(clean_name, has_HO_exemp, bill_cur, bill_new, bill_change, perceived_savings,  AV, `Taxable EAV`, everything()) |>
  select(-c(agency_number, agency_name))

muni_median_summarytable
Code
muni_taxrates <- ptax_pins |> 
  group_by(clean_name)  |>
  arrange(av) |>
  
  summarize(
    muni_median_av = round(median(av, na.rm=TRUE)),
    muni_median_eav = round(median(eav, na.rm=TRUE)),
    muni_mean_av = round(mean(av, na.rm=TRUE)),
    muni_mean_eav = round(mean(eav, na.rm=TRUE)),
    av = sum(av, na.rm = TRUE),
    eav = sum(eav, na.rm = TRUE),
    equalized_AV = sum(equalized_av, na.rm = TRUE),
    pins_in_muni = n(),
    current_exemptions = sum(all_exemptions, na.rm = TRUE),
    HO_exemps = sum(exe_homeowner, na.rm = TRUE),
    tax_code_rate = mean(tax_code_rate, na.rm = TRUE), # Changed from first() to mean() on Nov 1
    final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE), # used as LEVY amount!! 
    final_tax_to_tif = sum(final_tax_to_tif, na.rm = TRUE),
    tax_amt_exe = sum(tax_amt_exe, na.rm = TRUE), 
    tax_amt_pre_exe = sum(tax_amt_pre_exe, na.rm = TRUE), 
    tax_amt_post_exe = sum(tax_amt_post_exe, na.rm = TRUE),
    rpm_tif_to_cps = sum(rpm_tif_to_cps, na.rm = TRUE), # not used
    rpm_tif_to_rpm = sum(rpm_tif_to_rpm, na.rm=TRUE), # not used
    rpm_tif_to_dist = sum(rpm_tif_to_dist, na.rm=TRUE), # not used
    tif_share = mean(tif_share, na.rm=TRUE), # not used
  ) |>
  
  mutate(total_bill_current = final_tax_to_dist + final_tax_to_tif) |>
  rename(cur_comp_TC_rate = tax_code_rate) |>
  mutate(current_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100),
         new_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100) + HO_exemps) |>
  mutate(new_comp_TC_rate = (final_tax_to_dist / new_taxable_eav)*100) |>
  
  mutate(new_comp_TC_rate = ifelse(is.nan(new_comp_TC_rate), cur_comp_TC_rate, new_comp_TC_rate)) |> 
  
  left_join(C2_munistats) |>
  left_join(nicknames) |>
  mutate(rate_change = cur_comp_TC_rate - new_comp_TC_rate,
    nobillchange_propertyEAV = round(10000 * ((cur_comp_TC_rate/100) / (rate_change/100))),
    nochange_av = round(nobillchange_propertyEAV / eq_factor),
    nochange_ratio = nochange_av / median_av) |>
  select(clean_name, C2median_av = median_av, muni_median_av,  C2mean_av = avg_av, muni_mean_av, cur_comp_TC_rate, new_comp_TC_rate, current_taxable_eav, new_taxable_eav, everything())

muni_taxrates 
Code
muni_taxrates |> left_join(nicknames) |> select(clean_name, muni_median_av, C2median_av, nochange_av )
Code
muni_median_summarytable |> 
  left_join(muni_taxrates) |> 
  ungroup() |>
  select(clean_name, has_HO_exemp, bill_cur, bill_new, bill_change, perceived_savings,  AV, `Taxable EAV`, nochange_av, nochange_ratio) #|>
Code
ptax_pins |> 
  filter(clean_name %in% c("Chicago", "Dolton", "Glencoe")) |>
  filter(between(av, 14500, 15000)) |>
  filter(class_1dig == 2) |> 
  filter(exe_senior == 0 & exe_freeze == 0 ) |>
  group_by(clean_name, has_HO_exemp) |>
  arrange(av) |>
  
  summarize(
            median_bill = median(total_billed, na.rm=TRUE),
            median_AV = median(av, na.rm= TRUE),
            eav = median(eav, na.rm=TRUE),
            # class = median(class),
            type = "Current Exemptions",
            N = n())

Table 4.

Municipalities with the largest and smallest reductions in tax base (as a share of residential EAV due to exemptions) and median property values

Figure 10. Tax Burden Shift from Current GHE

Share of municipal property tax levy paid by Class 2 properties with and without homestead exemptions, tax year 2021

Code
# as a dot graph ## 

order <- mc_burden |>
  filter(class_1dig == 2) |>
    select(clean_name, pct_taxburden_current, burden_shift)

slice <-  mc_burden |>
  filter(class_1dig == 2) |>
    select(clean_name, pct_taxburden_current, burden_shift) |>
  arrange(pct_taxburden_current) |>
  slice(1:5, 63:67, 127:131)



median_burden <- median(order$pct_taxburden_current)
median_shift <- median(order$burden_shift)

# median burden change is 5.9 percentage points
# current median burden is 70.3% of the levy

mc_burden |> 
  filter(clean_name %in% slice$clean_name) |>
#filter(!clean_name %in% cross_county_lines$clean_name)|>
  filter(class_1dig == 2) |>
#  filter(burden_current > 0.938 |burden_current < .17 |
 #          ( (burden_current < median(burden_current) + 0.01 )& (burden_current > median(burden_current) - 0.01)) )|> 
  ungroup() |> 
      select(clean_name, pct_taxburden_current, hyp_pct_taxburden, burden_shift) |>
  arrange(burden_shift) |>
 # mutate(  burden_noexemps = ifelse(burden_noexemps > 1, 1, burden_noexemps)) |>
  pivot_longer(c("pct_taxburden_current", "hyp_pct_taxburden"), 
               names_to = "type", values_to = "pct_burden") |> 
  inner_join(order) |>
  ggplot(aes(x = pct_burden*100, 
             y= reorder(clean_name, -pct_taxburden_current)))+
  # y= reorder(clean_name, burden_current)))+
  geom_vline(xintercept = 70.2, linetype = 3)+
  geom_line(aes(group = clean_name))+ 
  geom_hline(yintercept = 5.5, linetype = 2)+
  geom_hline(yintercept = 10.5, linetype = 2)+
  geom_point(aes(color=type), size=3 )+

  theme_minimal() + 
  theme(#legend.position = "none", 
    legend.title = element_blank(),
    plot.title.position = "plot",
    #   panel.background = element_rect(fill='transparent'), #transparent panel bg
    plot.background = element_rect(fill='transparent', color=NA) #transparent plot bg
  )+
  scale_color_brewer(palette="Paired", labels = c("Current Burden", "Burden if \nNo Exemptions" ), direction = 1)+

  
  labs(title = "Change in Class 2 Residential Tax Burden", 
       subtitle = "Ordered by Current Tax Burden",
  x = "Share of Levy (%)", y = "" , 
  caption = "Dotted line represents median Class 2 burden (65.5% of the levy). Residential Tax Burden is the 
  share of the property tax collected that was paid for by property owners with Class 2 properties.") +
    geom_label(label = "Class 2 pays small share of \nlevy; very little residential", x=32, y = 13, label.size = 1, size = 3)+
    geom_label(label = "Class 2 pays median share of \nlevy (70.3%), mix of land use", x=42, y = 7.5, label.size = 1, size = 3) +
    geom_label(label = "Class 2 pays nearly all of levy, \nhighly residential", x=70, y = 3, label.size = 1,size = 3)
Figure 1.1

Figure 11. Zero Dollar Bills

Code
muni_mc_sums |>
  filter(major_class_code == 2) |> 
  group_by(year) |>
  summarize(zerodollar_count = sum(zero_bill)) |> 
  ggplot(aes(x=year, y = zerodollar_count)) + 
  geom_bar(position = "stack", stat = "identity") + theme_minimal()

Code
smallbills <- ptax_pins |> 
  filter(class_1dig == 2 & eav < 150 ) |>
  select(clean_name, class, eav, equalized_av, av, fmv, total_billed, 
         final_tax_to_dist, pin, everything()) |>
  arrange(total_billed) |> arrange(desc(total_billed)) |>
  filter(total_billed > 0)

smallbills
Code
smallbills |>
  summarize(n = n(), 
            exe_freeze = sum(exe_freeze, na.rm=T),
            exe_senior = sum(exe_senior, na.rm=T),
            exe_homeowner = sum(exe_homeowner, na.rm=T))
Code
smallbills |>
  group_by(clean_name) |>
  summarize(n = n(), 
            exe_freeze = sum(exe_freeze, na.rm=T),
            exe_senior = sum(exe_senior, na.rm=T),
            exe_homeowner = sum(exe_homeowner, na.rm=T))
Code
smallbills |> 
  select(clean_name, class, eav, equalized_av, av, fmv, 
         total_billed, final_tax_to_dist, pin)
Code
zerobills <- ptax_pins |> 
  filter(class_1dig == 2 & eav > 0 & total_billed == 0) |>
  select(clean_name, class, eav, equalized_av, av, fmv, total_billed, final_tax_to_dist, pin, exe_homeowner, exe_senior, exe_freeze, everything())  |>
  arrange(desc(exe_freeze))

zerobills
Code
options(scipen = 999)

zerobills |> 
  reframe(n = n(), total_eav = sum(taxed_eav + exempt_eav, na.rm=TRUE),
          exe_homeowner = sum(exe_homeowner), exe_senior = sum(exe_senior), 
          exe_freeze = sum(exe_freeze), taxed_eav = sum(taxed_eav, na.rm=TRUE), 
          .by = clean_name) |> 
  arrange(desc(exe_freeze))
Code
zerobills |> 
  summarize(n = n(), total_eav = sum(taxed_eav + exempt_eav, na.rm=TRUE),
          exe_homeowner = sum(exe_homeowner), exe_senior = sum(exe_senior), 
          exe_freeze = sum(exe_freeze), taxed_eav = sum(taxed_eav, na.rm=TRUE)) |> 
  arrange(desc(exe_freeze))
Code
zerobills |> 
  group_by(Triad) |>
  summarize(n = n(), total_eav = sum(taxed_eav + exempt_eav, na.rm=TRUE),
          exe_homeowner = sum(exe_homeowner), exe_senior = sum(exe_senior), 
          exe_freeze = sum(exe_freeze), taxed_eav = sum(taxed_eav, na.rm=TRUE)) |> 
  arrange(desc(exe_freeze))
Code
ptax_pins |> 
  filter(class_1dig == 2 & eav > 0 & total_billed == 0) |>
  select(clean_name, class, eav, equalized_av, av, fmv, total_billed, final_tax_to_dist, pin, exe_homeowner, exe_senior, exe_freeze)  |>
  arrange(desc(exe_freeze)) |>
  summarize(max = max(eav),
            min = min(eav),
            median = median(eav),
            n = n())